OpenRaiser/Intern-Atlas huggingface dataset:
Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 Triplet Scoring Challenge was to calculate relevance scores between an entity and its professions/nationalities. Such scores are a fundamental ingredient when ranking results in entity search. This paper proposes a novel approach to ensemble an advanced Knowledge Graph Embedding Model with a simple bag-of-words model. The former deals with hidden pragmatics and deep semantics whereas the latter handles text-based retrieval and low-level semantics.
Triple Scoring Using Paragraph Vector - The Gailan Triple Scorer at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

In this paper we describe our solution to the WSDM Cup 2017 Triple Scoring task. Our approach generates a relevance score based on the textual description of the triple's subject and value (Object). It measures how similar (related) the text description of the subject is to the text description of its values. The generated similarity score can then be used to rank the multiple values associated with this subject. We utilize the Paragraph Vector algorithm to represent the unstructured text into fixed length vectors. The fixed length representation is then employed to calculate the similarity (relevance) score between the subject and its multiple values. Our experimental results have shown that the suggested approach is promising and suitable to solve this problem.
A Compositional Coalgebraic Semantics of Strategic Games
Venue: arXiv cs.GT 2017
Year: 2017

We provide a compositional coalgebraic semantics for strategic games. In our framework, like in the semantics of functional programming languages, coalgebras represent the observable behaviour of systems derived from the behaviour of the parts over an unobservable state space. We use coalgebras to describe and program stage games, finitely and potentially infinitely repeated hierarchical or parallel games with imperfect and incomplete information based on deterministic, non-deterministic or probabilistic decisions of learning agents in possibly endogenous networks. Our framework is compositional in that arbitrarily complex network of games can be composed. The coalgebraic approach allows to represent self-referential or reflexive structures like institutional dynamics, strategic network formation from within the network, belief formation, learning agents or other self-referential phenomena that characterise complex social systems of cognitive agents. And finally our games represent directly runnable code in functional programming languages that can also be analysed by sophisticated verification and logical tools of software engineering.
Distributed Relay Selection for Heterogeneous UAV Communication Networks Using A Many-to-Many Matching Game Without Substitutability
Venue: arXiv cs.NI 2017
Year: 2017

This paper proposes a distributed multiple relay selection scheme to maximize the satisfaction experiences of unmanned aerial vehicles (UAV) communication networks. The multi-radio and multi-channel (MRMC) UAV communication system is considered in this paper. One source UAV can select one or more relay radios, and each relay radio can be shared by multiple source UAVs equally. Without the center controller, source UAVs with heterogeneous requirements compete for channels dominated by relay radios. In order to optimize the global satisfaction performance, we model the UAV communication network as a many-to-many matching market without substitutability. We design a potential matching approach to address the optimization problem, in which the optimizing of local matching process will lead to the improvement of global matching results. Simulation results show that the proposed distributed matching approach yields good matching performance of satisfaction, which is close to the global optimum result. Moreover, the many-to-many potential matching approach outperforms existing schemes sufficiently in terms of global satisfaction within a reasonable convergence time.
Novel Ranking-Based Lexical Similarity Measure for Word Embedding
Venue: arXiv cs.CL 2017
Year: 2017

Distributional semantics models derive word space from linguistic items in context. Meaning is obtained by defining a distance measure between vectors corresponding to lexical entities. Such vectors present several problems. In this paper we provide a guideline for post process improvements to the baseline vectors. We focus on refining the similarity aspect, address imperfections of the model by applying the hubness reduction method, implementing relational knowledge into the model, and providing a new ranking similarity definition that give maximum weight to the top 1 component value. This feature ranking is similar to the one used in information retrieval. All these enrichments outperform current literature results for joint ESL and TOEF sets comparison. Since single word embedding is a basic element of any semantic task one can expect a significant improvement of results for these tasks. Moreover, our improved method of text processing can be translated to continuous distributed representation of biological sequences for deep proteomics and genomics.
A simple script language for choreography of multiple, synchronizing non-anthropomorphic robots
Venue: arXiv cs.RO 2017
Year: 2017

The scripting language described in this document is (in the first place) intended to be used on robots developed by Anja Mølle Lindelof and Henning Christiansen as part of a research project about robots performing on stage. The target robots are expected to appear as familiar domestic objects that take their own life, so to speak, and perhaps perform together with human players, creating at illusion of a communication between them. In the current version, these robots' common behaviour is determined uniquely by a script written in the language described here -- the only possible autonomy for the robots is action to correct dynamically for inaccuracies that arise during a performance. The present work is preliminary and has not been compared to properly to other research work in this area, and the testing is still limited. A first implementation on small Lego Mindstorms based robots is under development by Mads Saustrup Fox as part of his master thesis work.
On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications
Venue: arXiv cs.DL 2017
Year: 2017

In this paper we present a first large-scale analysis of the relationship between Mendeley readership and citation counts with particular documents bibliographic characteristics. A data set of 1.3 million publications from different fields published in journals covered by the Web of Science (WoS) has been analyzed. This work reveals that document types that are often excluded from citation analysis due to their lower citation values, like editorial materials, letters, or news items, are strongly covered and saved in Mendeley, suggesting that Mendeley readership can reliably inform the analysis of these document types. Findings show that collaborative papers are frequently saved in Mendeley, which is similar to what is observed for citations. The relationship between readership and the length of titles and number of pages, however, is weaker than for the same relationship observed for citations. The analysis of different disciplines also points to different patterns in the relationship between several document characteristics, readership, and citation counts. Overall, results highlight that although disciplinary differences exist, readership counts are related to similar bibliographic characteristics as those related to citation counts, reinforcing the idea that Mendeley readership and citations capture a similar concept of impact, although they cannot be considered as equivalent indicators.
Study of Iterative Detection and Decoding for Large-Scale MIMO Systems with 1-Bit ADCs
Venue: arXiv cs.IT 2017
Year: 2017

We present a novel iterative detection and decoding scheme for the uplink of large-scale multiuser multiple-antenna systems. In order to reduce the receiver's energy consumption and computational complexity, 1-bit analog-to-digital converters are used in the front-end. The performance loss due to the 1-bit quantization can be mitigated by using large-scale antenna arrays. We propose a linear low-resolution-aware minimum mean square error detector for soft multiuser interference mitigation. Moreover, short block length low-density parity-check codes are considered for avoiding high latency. In the channel decoder, a quasi-uniform quantizer with scaling factors is devised to lower the error floor of LDPC codes. Simulations show good performance of the system in terms of bit error rate as compared to prior work.
Multiaccess Communication via a Broadcast Approach Adapted to the Multiuser Channel
Venue: arXiv cs.IT 2017
Year: 2017

A broadcast strategy for multiple access communication over slowly fading channels is introduced, in which the channel state information is known to only the receiver. In this strategy, the transmitters split their information streams into multiple independent information streams, each adapted to a specific actual channel realization. The major distinction between the proposed strategy and the existing ones is that in the existing approaches, each transmitter adapts its transmission strategy only to the fading process of its direct channel to the receiver, hence directly adopting a single-user strategy previously designed for the single-user channels. However, the contribution of each user to a network-wide measure (e.g., sum-rate capacity) depends not only on the user's direct channel to the receiver, but also on the qualities of other channels. Driven by this premise, this paper proposes an alternative broadcast strategy in which the transmitters adapt their transmissions to the combined states resulting from all users' channels. This leads to generating a larger number of information streams by each transmitter and adopting a different decoding strategy by the receiver. An achievable rate region and an outer bound that capture the trade-off among the rates of different information layers are established, and it is shown that the achievable rate region subsumes the existing known capacity regions obtained based on adapting the broadcast approach to the single-user channels.
Presburger-Definable Parameterized Typestates
Venue: arXiv cs.PL 2017
Year: 2017

Typestates are good at capturing dynamic states of a program as compared to normal types that can capture static structural properties of data and program. Although useful, typestates are suitable only for specifying and verifying program properties defined using finite-state abstractions. Many useful dynamic properties of programs are not finite-state definable. To address these issues, we introduce parameterized typestates (p-typestates). p-typestates associate a logical property with each state of regular typestate, thereby allowing specification of properties beyond finite-state abstractions. We present a dependent type system to express and verify p-typestate properties and a typestate-oriented core programming language incorporating these dependent types. Automatic inductive type-checking of p-typestate properties usually requires a programmer to provide loop invariants as annotations. Here we propose a way to calculate loop invariants automatically, using loop acceleration techniques for Presburger definable transition systems. \keywords{Programming Languages, Typestates, Dependent Types, Non-Regular Program Properties, Verification, Loop Invariants}
Semi-automatic definite description annotation: a first report
Venue: arXiv cs.CL 2017
Year: 2017

Studies in Referring Expression Generation (REG) often make use of corpora of definite descriptions produced by human subjects in controlled experiments. Experiments of this kind, which are essential for the study of reference phenomena and many others, may however include a considerable amount of noise. Human subjects may easily lack attention, or may simply misunderstand the task at hand and, as a result, the elicited data may include large proportions of ambiguous or ill-formed descriptions. In addition to that, REG corpora are usually collected for the study of semantics-related phenomena, and it is often the case that the elicited descriptions (and their input contexts) need to be annotated with their corresponding semantic properties. This, as in many other fields, may require considerable time and skilled annotators. As a means to tackle both kinds of difficulties - poor data quality and high annotation costs - this work discusses a semi-automatic method for the annotation of definite descriptions produced by human subjects in REG data collection experiments. The method makes use of simple rules to establish associations between words and meanings, and is intended to facilitate the design of experiments that produce REG corpora.
HelPal: A Search System for Mobile Crowd Service
Venue: arXiv cs.IR 2017
Year: 2017

Proliferation of ubiquitous mobile devices makes location based services prevalent. Mobile users are able to volunteer as providers of specific services and in the meanwhile to search these services. For example, drivers may be interested in tracking available nearby users who are willing to help with motor repair or are willing to provide travel directions or first aid. With the diffusion of mobile users, it is necessary to provide scalable means of enabling such users to connect with other nearby users so that they can help each other with specific services. Motivated by these observations, we design and implement a general location based system HelPal for mobile users to provide and enjoy instant service, which is called mobile crowd service. In this demo, we introduce a mobile crowd service system featured with several novel techniques. We sketch the system architecture and illustrate scenarios via several cases. Demonstration shows the user-friendly search interface for users to conveniently find skilled and qualified nearby service providers.
Deep Blind Image Inpainting
Venue: arXiv cs.CV 2017
Year: 2017

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing infor- mation in corrupted regions. However, directly using exist- ing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.
A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence
Venue: arXiv cs.AI 2017
Year: 2017

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.
A Formalization of Unique Solutions of Equations in Process Algebra
Venue: arXiv cs.LO 2017
Year: 2017

In this thesis, a comprehensive formalization of Milner's Calculus of Communicating Systems (also known as CCS) has been done in HOL theorem prover (HOL4), based on an old work in HOL88. This includes all classical properties of strong/weak bisimulation equivalences and observation congruence, a theory of congruence for CCS, various versions of "bisimulation up to" techniques, and several deep theorems, namely the "coarsest congruence contained in weak equivalence", and three versions of the "unique solution of equations" theorem in Milner's book. This work is further extended to support recent developments in Concurrency Theory, namely the "contraction" relation and the related "unique solutions of contractions" theorem found by Prof. Davide Sangiorgi, University of Bologna. As a result, a rather complete theory of "contraction" (and a similar relation called "expansion") for CCS is also formalized in this thesis. Further more, a new variant of contraction called "observational contraction" was found by the author during this work, based on existing contraction relation. It's formally proved that, this new relation is preserved by direct sums of CCS processes, and has a more elegant form of the "unique solutions of contractions" theorem without any restriction on the CCS grammar.
Multi-modal Geolocation Estimation Using Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion relies heavily on contextual information, and is not as simple as identifying a single object in the image. Thus any methods which attempt to do so must somehow account for these complexities, and no single model to date is completely capable of addressing all challenges. This work contributes to the state of research in image geolocation inferencing by introducing a novel global meshing strategy, outlining a variety of training procedures to overcome the considerable data limitations when training these models, and demonstrating how incorporating additional information can be used to improve the overall performance of a geolocation inference model. In this work, it is shown that Delaunay triangles are an effective type of mesh for geolocation in relatively low volume scenarios when compared to results from state of the art models which use quad trees and an order of magnitude more training data. In addition, the time of posting, learned user albuming, and other meta data are easily incorporated to improve geolocation by up to 11% for country-level (750 km) locality accuracy to 3% for city-level (25 km) localities.
Improving Text Normalization by Optimizing Nearest Neighbor Matching
Venue: arXiv cs.CL 2017
Year: 2017

Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text normalization approaches typically require manual selection of parameters for improved performance. In this paper, we present an automatic optimizationbased nearest neighbor matching approach for text normalization. This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it. Our similarity function incorporates weighted contributions of contextual, string, and phonetic similarity, and the nearest neighbor matching involves a minimum similarity threshold. These four parameters are tuned efficiently using grid search. We evaluate the performance of our approach on two benchmark datasets. The results demonstrate that parameter tuning on small sized labeled datasets produce state-of-the-art text normalization performances. Thus, this approach allows practically easy construction of evolving domain-specific normalization lexicons
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning
Venue: arXiv cs.LG 2017
Year: 2017

Physical activity and sleep play a major role in the prevention and management of many chronic conditions. It is not a trivial task to understand their impact on chronic conditions. Currently, data from electronic health records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid increase in the popularity of wearable health devices provides a significant new data source, making it possible to track the user's lifestyle real-time through web interfaces, both to consumer as well as their healthcare provider, potentially. However, at present there is a gap between lifestyle data (e.g., sleep, physical activity) and clinical outcomes normally captured in EHRs. This is a critical barrier for the use of this new source of signal for healthcare decision making. Applying deep learning to wearables data provides a new opportunity to overcome this barrier. To address the problem of the unavailability of clinical data from a major fraction of subjects and unrepresentative subject populations, we propose a novel unsupervised (task-agnostic) time-series representation learning technique called act2vec. act2vec learns useful features by taking into account the co-occurrence of activity levels along with periodicity of human activity patterns. The learned representations are then exploited to boost the performance of disorder-specific supervised learning models. Furthermore, since many disorders are often related to each other, a phenomenon referred to as co-morbidity, we use a multi-task learning framework for exploiting the shared structure of disorder inducing life-style choices partially captured in the wearables data. Empirical evaluation using actigraphy data from 4,124 subjects shows that our proposed method performs and generalizes substantially better than the conventional time-series symbolic representational methods and task-specific deep learning models.
FogGrid: Leveraging Fog Computing for Enhanced Smart Grid Network
Venue: arXiv cs.DC 2017
Year: 2017

The present manuscript concentrates on the application of Fog computing to a Smart Grid Network that comprises of a Distribution Generation System known as a Microgrid. It addresses features and advantages of a smart grid. Two computational methods for on-demand processing based on shared information resources is discussed. Fog Computing acts as an additional layer of computational and/or communication nodes that offload the Cloud backend from multi-tasking while dealing with large amounts of data. Both Fog computing and Cloud computing hierarchical architecture is compared with respect to efficient utilization of resources. To alleviate the advantages of Fog computing, a Fog computing framework based on Intel Edison is proposed. The proposed architecture has been hardware implemented for a microgrid system. The results obtained show the efficacy of Fog Computing for smart grid network in terms of low power consumption, reduced storage requirement and overlay analysis capabilities.